Abstract Depression commonly emerges in youth and is associated with disability and suicide. Early detection and intervention has the promise of altering trajectories, improving adult outcomes and preventing suicide. Brain networks implicated in depression (frontal-limbic threat, cortico-striatal approach/reward and default mode) undergo significant change during childhood and adolescence; the manner of how these changes unfold may be critical to understanding the onset and course of depression and suicide risk. The Adolescent Brain Cognitive Development (ABCD) is a population-based study that is following over 11,000 children annually over 10 years, with clinical and neuroimaging data from the first two years already publicly available. Since ABCD data collection spans the peak onset of adolescent depression, analyzing this data presents an ideal opportunity to characterize the links between neural network changes and unfolding depression and suicide risk in youth. Resting-state fMRI (rs-fMRI) can be used to characterize brain network structure and organization. While our laboratory and others have extensively applied standard functional connectivity methods to characterize strength within depression networks using cross-sectional designs, longitudinal designs are needed to understand now aberrant development in network strength may contribute to depression onset. More recently, novel approaches have emerged to use rs-fMRI data to estimate network flexibility, which provide a more accurate characterization of brain networks. These include drawing from information theory to measure the entropy of brain signals and from dynamic connectivity analyses to measure the temporal variability and state-switching of brain networks. Our preliminary data suggests that depression and suicidal risk in adolescents correlate with reduced flexibility estimates (lower entropy in limbic regions, lower state-switching frequency), suggesting that depression is associated with a tendency to get ?stuck? in particular network configurations. Developmental research has begun to document how neural network strength and flexibility change across childhood and adolescence; we propose that individual differences in the trajectory of these changes may help explain the emergence of depression and suicide risk in adolescents. In our conceptual model, inherited and environmental factors shape network developmental trajectories, which in turn underlie the emergence of depression and suicide risk. The current proposal seeks to delineate the neurodevelopmental trajectories of strength and flexibility in fronto-limbic threat, cortico-striatal approach/reward and default mode networks associated with the risk, onset and early course of depression, self-harm and suicide risk in children and adolescents in the ABCD study using novel analytic strategies. New insights from this study will provide the foundation for designing personalized interventions to facilitate early detection of depression and suicide risk, and to guide interventions capable of restoring healthy brain development and averting serious negative outcomes including suicide.